Development of Degradation Evaluation SW for High Temperature Component using Machine Learning Approach

Replication is the most commonly used method in industry to assess the extent of damage and degradation of high temperature components. However, it is quite difficult to distinguish degradation levels because of the high level of uncertainty associated with the subject of the evaluator. Therefore, a more quantitative and accurate objective degradation evaluation method is necessary. In this paper, we propose a machine learning-based degradation evaluation program that evaluates the degradation grade of high temperature components using a support vector machine, which has excellent effect in classification problem among machine learning method. Also, open source image processing library is used. We verified the accuracy of the developed program by performing a degradation evaluation using image data of which the degradation grade was known. We demonstrate that the machine learning-based degradation evaluation program enables objective, quick and accurate deterioration evaluation a level that surpasses the capability of existing expert judgment.